Gene Expression Analysis Using R

Today’s Objectives
Cleft Lip and Palate 1/3
Cleft Lip and Palate 2/3
Cleft Lip and Palate 3/3
Question
Hint
Hypothesis
Why Microarray?
Original Paper
Experimental Design
Dataset
Loading
Checking
Exploring
Transforming
Multiple Plots 1/2
Multiple Plots 2/2
Boxplot
Clustering 1/2
Clustering 2/2
Comparing
Scatter 1/2
Scatter 2/2
Differentially Expressed Genes (DEGs)
Biological Significance (fold-change) 1/2
Biological Significance (fold-change) 2/2
Statistical Significance (p-value) 1/3
t-test
Case A
Case B
Statistical Significance (p-value) 2/3
Statistical Significance (p-value) 3/3
Volcano : Statistical & Biological 1/3
Volcano : Statistical & Biological 2/3
Volcano : Statistical & Biological 3/3
Filtering for DEGs 1/3
Filtering for DEGs 2/3
Filtering for DEGs 3/3
Exercise
Solution 1/2
Solution 2/2
Heatmap 1/5
Heatmap 2/5
Heatmap 3/5
Heatmap 4/5
Heatmap 5/5
Enhanced Heatmap 1/4
Enhanced Heatmap 2/4
Enhanced Heatmap 3/4
Enhanced Heatmap 4/4
Annnotation 1/3
Annnotation 2/3
Annnotation 3/3
Exercise
Solution 1/2
Solution 2/2
Homework
Thank you!

Today’s Objectives

Download and load microarray dataset into R
Explore the dataset with basic visualizations
Identify differentially expressed genes (DEGs)
Generate annotation of the DEGs

Cleft Lip and Palate 1/3

Cleft lip and cleft palate (CLP) are splits in the upper lip, the roof of the mouth (palate) or both. They result when facial structures that are developing in an unborn baby do not close completely. CLP is one of the most common birth defects with a frequency of 1/700 live births.

Cleft lip and palate

Cleft Lip and Palate 2/3

Children with cleft lip with or without cleft palate face a variety of challenges, depending on the type and severity of the cleft.

Difficulty feeding. One of the most immediate concerns after birth is feeding.
Ear infections and hearing loss. Babies with cleft palate are especially at risk of developing middle ear fluid and hearing loss.
Dental problems. If the cleft extends through the upper gum, tooth development may be affected.
Speech difficulties. Because the palate is used in forming sounds, the development of normal speech can be affected by a cleft palate. Speech may sound too nasal.

Reference: Mayo Foundation for Medical Education and Research

Cleft Lip and Palate 3/3

DNA variation in Interferon Regulatory Factor 6 (IRF6) causes Van der Woude syndrome (VWS)
VWS is the most common syndromic form of cleft lip and palate.
However, the causing variant in IRF6 has been found in only 70% of VWS families!
IRF6 is a transcription factor with a conserved helix-loop-helix DNA binding domain and a less well-conserved protein binding domain.

Reference: Hum Mol Genet. 2014 May 15; 23(10): 2711–2720

Question

Given:

The pathogenic variant in IRF6 exists in only 70% of the VWS families
IRF6 is a transcription factor

How can we identify other genes that might be involved in the remaining 30% of the VWS families?

Hint

Usually, genes that are regulated by a transcription factor belong to the same biological process or pathway.
Therefore, by comparing the gene expression patterns between wild-type (functional) Irf6 and knockout (non-functional) Irf6, it could be possible to identify genes that are regulated (targeted) by Irf6.

Hypothesis

$H_O : \mu_{WT} = \mu_{KO}$
$H_A : \mu_{WT} \ne \mu_{KO}$
Where $\mu$ is the mean of the gene expression values of a gene.
One-sided or Two-sided testing?

Why Microarray?

Original Paper

PMID: 17041601

Experimental Design

3 IRF6 wild-type (+/+) and 3 knockout (-/-) mouse embryos.
E17.5 embryos were removed from euthanized mothers.
Skin was removed from embryos.
Total RNA was isolated from the skin.
Resultant RNA was hybridized to Affymetrix GeneChip Mouse Genome 430 2.0 arrays.

Dataset

The original dataset can be obtained from NCBI GEO with accession GSE5800

ID	KO1	KO2	KO3	WT1	WT2	WT3
1415670_at	6531.0	5562.8	6822.4	7732.1	7191.2	7551.9
1415671_at	11486.3	10542.7	10641.4	10408.2	9484.5	7650.2
1415672_at	14339.2	13526.1	14444.7	12936.6	13841.7	13285.7
1415673_at	3156.8	2219.5	3264.4	2374.2	2201.8	2525.3

Download the dataset from the following link https://goo.gl/gH7QLM

Loading

We are going to load the dataset from a tsv file (Irf6.tsv) into a variable called data using function read.table.

data here is just an arbitrary varilable name to hold the result of read.table and it can be called/named almost anything. See The State of Naming Conventions in R (Bååth 2012) for more information on naming varilables in R.

# Load data from text file into a varilable
data = as.matrix(read.table("Irf6.tsv", header = TRUE, row.names = 1))

Note: the hash sign (#) indicates that what comes after is a comment. Comments are for documentation and readability of the R code and they are not evaluated (or executed).

Checking

dim(data) # Dimension of the dataset

## [1] 45101     6

head(data) # First few rows

	KO1	KO2	KO3	WT1	WT2	WT3
1415670_at	6531.0	5562.8	6822.4	7732.1	7191.2	7551.9
1415671_at	11486.3	10542.7	10641.4	10408.2	9484.5	7650.2
1415672_at	14339.2	13526.1	14444.7	12936.6	13841.7	13285.7
1415673_at	3156.8	2219.5	3264.4	2374.2	2201.8	2525.3
1415674_a_at	4002.0	3306.9	3777.0	3760.6	3137.0	2911.5
1415675_at	3468.4	3347.4	3332.9	3073.5	3046.0	2914.4

Exploring

Check the behavior of the data (e.g., normal?, skewed?)

hist(data, col = "gray", main="Histogram")

Transforming

$log_2$ transformation (why?)

data2 = log2(data)
hist(data2, col = "gray", main="Histogram")

Multiple Plots 1/2

samples = colnames(data2) # Headers (names) of the columns
samples

## [1] "KO1" "KO2" "KO3" "WT1" "WT2" "WT3"

par( mfrow = c( 2, 3 ) ) # Split screen into 2 rows x 3 columns paritions

for (i in 1:3) {
  # for each of the first 3 columns in the table
  hist(data2[,i], col = "red", main = samples[i])
}

for (i in 4:6) {
  # for each of the last 3 columns in the table
  hist(data2[,i], col = "green", main = samples[i])
}

Multiple Plots 2/2

par( mfrow = c( 1, 1 ) ) # Just to set screen back to 1 partition

Boxplot

colors = c(rep("red", 3), rep("green", 3))
boxplot(data2, col = colors, las = 2)

Clustering 1/2

Hierarchical clustering of the samples (i.e., columns) based on the correlation coefficients of the expression values

hc = hclust(as.dist(1 - cor(data2)))
plot(hc)

Clustering 2/2

To learn about a function (e.g., hclust), you may type ?function (e.g., ?hclust) in the console to launch R documentation on that function:

Comparing

We are going to compare the means of the replicates of the two conditions

# Compute the means of the samples of each condition
ko = apply(data2[, 1:3], 1, mean)
head(ko)

##   1415670_at   1415671_at   1415672_at   1415673_at 1415674_a_at 
##     12.61692     13.40966     13.78313     11.47096     11.84693 
##   1415675_at 
##     11.72381

wt = apply(data2[, 4:6], 1, mean)
head(wt)

##   1415670_at   1415671_at   1415672_at   1415673_at 1415674_a_at 
##     12.87043     13.15269     13.70450     11.20664     11.66649 
##   1415675_at 
##     11.55578

Scatter 1/2

Scatter 2/2

pairs(data2) # All pairwise comparisons

Differentially Expressed Genes (DEGs)

To identify DEGs, we will identify:

Biologically significantly differentially expressed
Statistically significantly differentially expressed

Then, we will take the overlap (intersection) of the two sets

Biological Significance (fold-change) 1/2

fold = ko - wt # Difference between means
head(fold)

##   1415670_at   1415671_at   1415672_at   1415673_at 1415674_a_at 
##  -0.25351267   0.25697097   0.07863227   0.26431191   0.18044345 
##   1415675_at 
##   0.16803065

What do the positive and negative values of the fold-change indicate? Considering the WT condition is the reference (or control)

+ve $\rightarrow$ Up-regulation $\uparrow$

-ve $\rightarrow$ Down-regulation $\downarrow$

Biological Significance (fold-change) 2/2

hist(fold, col = "gray") # Histogram of the fold

Statistical Significance (p-value) 1/3

To assess the statistical significance of the difference in the expression values for each gene between the two conditions (e.g., WT and KO), we are going to use t-test.

t-test

Let’s say there are two samples x and y from the two populations, X and Y, respectively, to determine whether the means of two populations are significantly different:

x = c(1, 3, 5, 7, 9)
y = c(2, 4, 6, 8, 10)
t.test(x, y)

## 
##  Welch Two Sample t-test
## 
## data:  x and y
## t = -0.5, df = 8, p-value = 0.6305
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -5.612008  3.612008
## sample estimates:
## mean of x mean of y 
##         5         6

Case A

broom::tidy(t.test(x, y))

estimate	estimate1	estimate2	statistic	p.value	parameter	conf.low	conf.high	method	alternative
0.3419216	-0.3426252	-0.6845468	0.7582038	0.4583316	17.65468	-0.6068459	1.290689	Welch Two Sample t-test	two.sided

Case B

broom::tidy(t.test(x, y))

estimate	estimate1	estimate2	statistic	p.value	parameter	conf.low	conf.high	method	alternative
4.124747	1.768308	-2.356439	8.052339	3e-07	17.5567	3.046615	5.202879	Welch Two Sample t-test	two.sided

Statistical Significance (p-value) 2/3

pvalue = NULL # Empty list for the p-values
n = nrow(data) # Number of genes (rows)
n

## [1] 45101

for(i in 1 : n) {
  # for each gene
  x = data2[i, 1:3] # wt values of gene number i
  y = data2[i, 4:6] # ko values of gene number i
  t = t.test(x, y) # t-test between the two conditions
  pvalue[i] = t$p.value # Put current p-value in the list of p-values
}
head(pvalue)

## [1] 0.092706280 0.182663337 0.129779075 0.272899180 0.262377176 0.005947807

Statistical Significance (p-value) 3/3

hist(-log10(pvalue), col = "gray") # Histogram of p-values (-log10)

Volcano : Statistical & Biological 1/3

plot(-log10(pvalue) ~ fold)

Volcano : Statistical & Biological 2/3

fold_cutoff = 2
pvalue_cutoff = 0.01

plot(-log10(pvalue) ~ fold)

abline(v = fold_cutoff, col = "blue", lwd = 3)
abline(v = -fold_cutoff, col = "red", lwd = 3)
abline(h = -log10(pvalue_cutoff), col = "green", lwd = 3)

Volcano : Statistical & Biological 3/3

Filtering for DEGs 1/3

filter_by_fold = abs(fold) >= fold_cutoff # Biological
sum(filter_by_fold) # Number of genes staisfy the condition

## [1] 1051

filter_by_pvalue = pvalue <= pvalue_cutoff # Statistical
sum(filter_by_pvalue)

## [1] 1564

filter_combined = filter_by_fold & filter_by_pvalue # Combined
sum(filter_combined)

## [1] 276

Filtering for DEGs 2/3

filtered = data2[filter_combined, ]
dim(filtered)

## [1] 276   6

head(filtered)

	KO1	KO2	KO3	WT1	WT2	WT3
1416200_at	13.312004	12.973357	12.868456	7.40429	8.558803	8.683696
1416236_a_at	14.148397	14.039236	14.130007	12.23604	12.022403	11.495055
1417808_at	5.321928	5.442944	4.053111	15.16978	15.070087	14.753274
1417932_at	10.602884	10.257152	10.496055	13.98445	14.203295	13.720960
1418050_at	10.622052	10.975490	10.795066	12.86513	13.012048	12.658122
1418100_at	9.117903	8.634811	9.057721	12.90358	12.842449	12.233769

Filtering for DEGs 3/3

plot(-log10(pvalue) ~ fold)
points(-log10(pvalue[filter_combined]) ~ fold[filter_combined],
       col = "green")

Exercise

On the volcano plot, highlight the up-regulated genes in red and the download-regulated genes in blue

Solution 1/2

Up-regulated genes

# Screen for the up-regulated genes (+ve fold)
filter_up = filter_combined & fold > 0

head(filter_up)

##   1415670_at   1415671_at   1415672_at   1415673_at 1415674_a_at 
##        FALSE        FALSE        FALSE        FALSE        FALSE 
##   1415675_at 
##        FALSE

# Number of filtered genes
sum(filter_up)

## [1] 95

Down-regulated genes

# Screen for the down-regulated genes (-ve fold)
filter_down = filter_combined & fold < 0

head(filter_down)

##   1415670_at   1415671_at   1415672_at   1415673_at 1415674_a_at 
##        FALSE        FALSE        FALSE        FALSE        FALSE 
##   1415675_at 
##        FALSE

# Number of filtered genes
sum(filter_down)

## [1] 181

Solution 2/2

plot(-log10(pvalue) ~ fold)
points(-log10(pvalue[filter_up]) ~ fold[filter_up], col = "red")
points(-log10(pvalue[filter_down]) ~ fold[filter_down], col = "blue")

Heatmap 1/5

heatmap(filtered)

Heatmap 2/5

By default, heatmap clusters genes (rows) and samples (columns) based on the Euclidean distance.
In the context of gene expression, it is more appropriate to cluster the genes and samples based on the correlation to explore patterns of co-regulation (co-expression) among the genes and similarities between samples.
To let heatmap cluster the genes and/or samples, we need to cluster the genes and samples by correlation coefficients (using cor) among the genes and samples.

Heatmap 3/5

# Clustering of the columns (samples)
col_dendrogram = as.dendrogram(hclust(as.dist(1-cor(filtered))))
plot(col_dendrogram)

Heatmap 4/5

# Clustering of the rows (genes)
row_dendrogram = as.dendrogram(hclust(as.dist(1-cor(t(filtered)))))
plot(row_dendrogram)

Heatmap 5/5

# Heatmap with the rows and columns clustered by correlation coefficients
heatmap(filtered, Rowv=row_dendrogram, Colv=col_dendrogram)

Enhanced Heatmap 1/4

There is an enhanced heatmap function heatmap.2 that comes with the gplots package. heatmap.2 provides more options via a richer set of parameters.

install.packages("gplots") # Install the package (if not already installed)

library(gplots) # Load the package

## 
## Attaching package: 'gplots'

## The following object is masked from 'package:stats':
## 
##     lowess

Enhanced Heatmap 2/4

heatmap.2(filtered, Rowv=row_dendrogram, Colv=col_dendrogram)

Enhanced Heatmap 3/4

heatmap.2(filtered, Rowv=row_dendrogram, Colv=col_dendrogram,
          col = bluered(256))

Enhanced Heatmap 4/4

heatmap.2(filtered, Rowv=row_dendrogram, Colv=col_dendrogram,
          col = bluered(256), scale = "row")

Annnotation 1/3

To obtain the functional annotation of the differentially expressed genes, we are going first to extract their probe ids:

filterd_ids = row.names(filtered) # ids of the filtered DE genes
length(filterd_ids)

## [1] 276

head(filterd_ids)

## [1] "1416200_at"   "1416236_a_at" "1417808_at"   "1417932_at"  
## [5] "1418050_at"   "1418100_at"

Then we can obtain the annotation online via The Database for Annotation, Visualization and Integrated Discovery DAVID.

write.table(filterd_ids, file = "filterd_ids.txt", quote = FALSE)

Annnotation 2/3

Alternatively, we can generate a comprehensive functional annotation via BioConductor packages annaffy and mouse4302.db.

To install BioConductor packages (if they are not already installed):

source("http://bioconductor.org/biocLite.R")
biocLite(c("annaffy", "mouse4302.db"))

Load the packages and extract annotation of the filtered ids:

library(annaffy)
library(hgu133plus2.db)
annotation_table = aafTableAnn(filterd_ids, "mouse4302.db")
saveHTML(annotation_table, file="filtered.html")
browseURL("filtered.html")

## Loading required package: Biobase

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## The following objects are masked from 'package:dplyr':
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## Loading required package: KEGG.db

## 
## KEGG.db contains mappings based on older data because the original
##   resource was removed from the the public domain before the most
##   recent update was produced. This package should now be
##   considered deprecated and future versions of Bioconductor may
##   not have it available.  Users who want more current data are
##   encouraged to look at the KEGGREST or reactome.db packages

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## Warning in rsqlite_fetch(res@ptr, n = n): Don't need to call dbFetch() for
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## An object of class "aafTable"
## Slot "probeids":
## [1] "1416200_at"
## 
## Slot "table":
## $Probe
## An object of class "aafList"
## [[1]]
## An object of class "aafProbe"
## [1] "1416200_at"
## 
## 
## $Symbol
## An object of class "aafList"
## [[1]]
## [1] "Il33"
## attr(,"class")
## [1] "aafSymbol"
## 
## 
## $Description
## An object of class "aafList"
## [[1]]
## [1] "interleukin 33"
## attr(,"class")
## [1] "aafDescription"
## 
## 
## $Chromosome
## An object of class "aafList"
## [[1]]
## [1] "19"
## attr(,"class")
## [1] "aafChromosome"
## 
## 
## $`Chromosome Location`
## An object of class "aafList"
## [[1]]
## An object of class "aafChromLoc"
## [1] 29945789 29925113
## 
## 
## $GenBank
## An object of class "aafList"
## [[1]]
## [1] "NM_133775"
## attr(,"class")
## [1] "aafGenBank"
## 
## 
## $Gene
## An object of class "aafList"
## [[1]]
## An object of class "aafLocusLink"
## [1] 77125
## 
## 
## $UniGene
## An object of class "aafList"
## [[1]]
## [1] "Mm.182359"
## attr(,"class")
## [1] "aafUniGene"
## 
## 
## $PubMed
## An object of class "aafList"
## [[1]]
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## [260] 27053610 27091974 27126934 27155324 27184849 27453471 27608599
## [267] 27626380
## 
## 
## $`Gene Ontology`
## An object of class "aafList"
## [[1]]
## An object of class "aafGO"
## [[1]][[1]]
## An object of class "aafGOItem"
## @id   "GO:0002281"
## @name "macrophage activation involved in immune response"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[2]]
## An object of class "aafGOItem"
## @id   "GO:0002282"
## @name "microglial cell activation involved in immune response"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[3]]
## An object of class "aafGOItem"
## @id   "GO:0002686"
## @name "negative regulation of leukocyte migration"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[4]]
## An object of class "aafGOItem"
## @id   "GO:0002826"
## @name "negative regulation of T-helper 1 type immune response"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[5]]
## An object of class "aafGOItem"
## @id   "GO:0002830"
## @name "positive regulation of type 2 immune response"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[6]]
## An object of class "aafGOItem"
## @id   "GO:0002830"
## @name "positive regulation of type 2 immune response"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[7]]
## An object of class "aafGOItem"
## @id   "GO:0005125"
## @name "cytokine activity"
## @type "Molecular Function"
## @evid "IDA"
## 
## [[1]][[8]]
## An object of class "aafGOItem"
## @id   "GO:0005125"
## @name "cytokine activity"
## @type "Molecular Function"
## @evid "IPI"
## 
## [[1]][[9]]
## An object of class "aafGOItem"
## @id   "GO:0005125"
## @name "cytokine activity"
## @type "Molecular Function"
## @evid "ISO"
## 
## [[1]][[10]]
## An object of class "aafGOItem"
## @id   "GO:0005576"
## @name "extracellular region"
## @type "Cellular Component"
## @evid "IEA"
## 
## [[1]][[11]]
## An object of class "aafGOItem"
## @id   "GO:0005615"
## @name "extracellular space"
## @type "Cellular Component"
## @evid "IEA"
## 
## [[1]][[12]]
## An object of class "aafGOItem"
## @id   "GO:0005634"
## @name "nucleus"
## @type "Cellular Component"
## @evid "ISO"
## 
## [[1]][[13]]
## An object of class "aafGOItem"
## @id   "GO:0005654"
## @name "nucleoplasm"
## @type "Cellular Component"
## @evid "ISO"
## 
## [[1]][[14]]
## An object of class "aafGOItem"
## @id   "GO:0005694"
## @name "chromosome"
## @type "Cellular Component"
## @evid "IEA"
## 
## [[1]][[15]]
## An object of class "aafGOItem"
## @id   "GO:0005829"
## @name "cytosol"
## @type "Cellular Component"
## @evid "ISO"
## 
## [[1]][[16]]
## An object of class "aafGOItem"
## @id   "GO:0006351"
## @name "transcription, DNA-templated"
## @type "Biological Process"
## @evid "IEA"
## 
## [[1]][[17]]
## An object of class "aafGOItem"
## @id   "GO:0010628"
## @name "positive regulation of gene expression"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[18]]
## An object of class "aafGOItem"
## @id   "GO:0031410"
## @name "cytoplasmic vesicle"
## @type "Cellular Component"
## @evid "IEA"
## 
## [[1]][[19]]
## An object of class "aafGOItem"
## @id   "GO:0032436"
## @name "positive regulation of proteasomal ubiquitin-dependent protein catabolic process"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[20]]
## An object of class "aafGOItem"
## @id   "GO:0032436"
## @name "positive regulation of proteasomal ubiquitin-dependent protein catabolic process"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[21]]
## An object of class "aafGOItem"
## @id   "GO:0032689"
## @name "negative regulation of interferon-gamma production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[22]]
## An object of class "aafGOItem"
## @id   "GO:0032736"
## @name "positive regulation of interleukin-13 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[23]]
## An object of class "aafGOItem"
## @id   "GO:0032753"
## @name "positive regulation of interleukin-4 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[24]]
## An object of class "aafGOItem"
## @id   "GO:0032754"
## @name "positive regulation of interleukin-5 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[25]]
## An object of class "aafGOItem"
## @id   "GO:0032755"
## @name "positive regulation of interleukin-6 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[26]]
## An object of class "aafGOItem"
## @id   "GO:0043032"
## @name "positive regulation of macrophage activation"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[27]]
## An object of class "aafGOItem"
## @id   "GO:0043032"
## @name "positive regulation of macrophage activation"
## @type "Biological Process"
## @evid "ISO"
## 
## [[1]][[28]]
## An object of class "aafGOItem"
## @id   "GO:0045944"
## @name "positive regulation of transcription from RNA polymerase II promoter"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[29]]
## An object of class "aafGOItem"
## @id   "GO:0050729"
## @name "positive regulation of inflammatory response"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[30]]
## An object of class "aafGOItem"
## @id   "GO:0051024"
## @name "positive regulation of immunoglobulin secretion"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[31]]
## An object of class "aafGOItem"
## @id   "GO:0051025"
## @name "negative regulation of immunoglobulin secretion"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[32]]
## An object of class "aafGOItem"
## @id   "GO:0051607"
## @name "defense response to virus"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[33]]
## An object of class "aafGOItem"
## @id   "GO:0061518"
## @name "microglial cell proliferation"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[34]]
## An object of class "aafGOItem"
## @id   "GO:0090197"
## @name "positive regulation of chemokine secretion"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[35]]
## An object of class "aafGOItem"
## @id   "GO:0090197"
## @name "positive regulation of chemokine secretion"
## @type "Biological Process"
## @evid "ISO"
## 
## [[1]][[36]]
## An object of class "aafGOItem"
## @id   "GO:0097191"
## @name "extrinsic apoptotic signaling pathway"
## @type "Biological Process"
## @evid "IGI"
## 
## 
## 
## $Pathway
## An object of class "aafList"
## [[1]]
## An object of class "aafPathway"
## [[1]][[1]]
## An object of class "aafGOItem"
## @id     "04623"
## @name   "Cytosolic DNA-sensing pathway"
## @enzyme ""

Annnotation 3/3

$Annotation$

Annotation

Exercise

Generate separate annotations for the up-regulated and down-regulated differentially expressed genes between WT and KO. Discuss each set in a molecular context as detailed as possible.

Solution 1/2

Generating two lists of IDs for the up- and down- regulated genes

ids = row.names(data2) # All the IDs
length(ids)

## [1] 45101

up_ids = ids[filter_up] # IDs of up-regulated genes
length(up_ids)

## [1] 95

up_ids

##  [1] "1416200_at"   "1416236_a_at" "1418203_at"   "1419394_s_at"
##  [5] "1419427_at"   "1419591_at"   "1420006_at"   "1420064_s_at"
##  [9] "1420431_at"   "1420753_at"   "1421156_a_at" "1421346_a_at"
## [13] "1421456_at"   "1421551_s_at" "1421676_at"   "1422588_at"  
## [17] "1422672_at"   "1422783_a_at" "1422784_at"   "1422892_s_at"
## [21] "1422983_at"   "1423271_at"   "1425002_at"   "1425452_s_at"
## [25] "1426252_a_at" "1426600_at"   "1426911_at"   "1427700_x_at"
## [29] "1428088_at"   "1429408_at"   "1430800_at"   "1431093_at"  
## [33] "1431609_a_at" "1431654_at"   "1432742_at"   "1433005_at"  
## [37] "1434346_at"   "1435639_at"   "1437258_at"   "1437518_at"  
## [41] "1438219_at"   "1438850_at"   "1439016_x_at" "1439149_s_at"
## [45] "1439347_at"   "1439458_x_at" "1440147_at"   "1440589_at"  
## [49] "1441011_at"   "1441197_at"   "1441596_at"   "1441772_at"  
## [53] "1442936_at"   "1444254_at"   "1444650_at"   "1445310_at"  
## [57] "1445349_at"   "1445378_at"   "1446498_at"   "1446858_at"  
## [61] "1446976_at"   "1447722_at"   "1448265_x_at" "1448756_at"  
## [65] "1448932_at"   "1449417_at"   "1449555_a_at" "1449873_at"  
## [69] "1449994_at"   "1450004_at"   "1450019_at"   "1450571_a_at"
## [73] "1450618_a_at" "1450811_at"   "1451006_at"   "1451453_at"  
## [77] "1451594_s_at" "1451757_at"   "1452487_x_at" "1453238_s_at"
## [81] "1453326_at"   "1453916_at"   "1455872_at"   "1456877_at"  
## [85] "1457016_at"   "1457550_at"   "1457666_s_at" "1458085_at"  
## [89] "1458147_at"   "1459227_at"   "1459306_at"   "1459713_s_at"
## [93] "1459982_a_at" "1460372_at"   "1460514_s_at"

down_ids = ids[filter_down] # IDs of down-regulared genes
length(down_ids)

## [1] 181

down_ids

##   [1] "1417808_at"   "1417932_at"   "1418050_at"   "1418100_at"  
##   [5] "1418213_at"   "1418266_at"   "1418301_at"   "1418609_at"  
##   [9] "1418645_at"   "1418855_at"   "1419317_x_at" "1419409_at"  
##  [13] "1419431_at"   "1419532_at"   "1419588_at"   "1419731_at"  
##  [17] "1420141_at"   "1420183_at"   "1420350_at"   "1420550_at"  
##  [21] "1420677_x_at" "1420741_x_at" "1421091_at"   "1421092_at"  
##  [25] "1421473_at"   "1421500_at"   "1422196_at"   "1422222_at"  
##  [29] "1422583_at"   "1422820_at"   "1422871_at"   "1422876_at"  
##  [33] "1422904_at"   "1423320_at"   "1423476_at"   "1423542_at"  
##  [37] "1423634_at"   "1424090_at"   "1424263_at"   "1424409_at"  
##  [41] "1424683_at"   "1425321_a_at" "1426337_a_at" "1426507_at"  
##  [45] "1426511_at"   "1427082_at"   "1428355_at"   "1428662_a_at"
##  [49] "1428882_at"   "1428980_at"   "1429565_s_at" "1429676_at"  
##  [53] "1429702_at"   "1429802_at"   "1429869_at"   "1429964_at"  
##  [57] "1430087_at"   "1430256_at"   "1430306_a_at" "1430513_at"  
##  [61] "1430594_at"   "1430661_at"   "1430715_at"   "1430730_at"  
##  [65] "1430952_at"   "1431426_at"   "1431554_a_at" "1431754_at"  
##  [69] "1431821_a_at" "1431829_a_at" "1431936_a_at" "1432058_at"  
##  [73] "1432558_a_at" "1432787_at"   "1432976_at"   "1433226_at"  
##  [77] "1433801_at"   "1433858_at"   "1433923_at"   "1434083_a_at"
##  [81] "1434092_at"   "1434146_at"   "1434271_at"   "1434725_at"  
##  [85] "1434917_at"   "1435191_at"   "1435462_at"   "1435482_at"  
##  [89] "1435541_at"   "1435572_at"   "1436614_at"   "1436917_s_at"
##  [93] "1436976_a_at" "1437145_s_at" "1437269_at"   "1437662_at"  
##  [97] "1437672_at"   "1437866_at"   "1437893_at"   "1438665_at"  
## [101] "1438849_at"   "1439836_at"   "1439878_at"   "1440887_at"  
## [105] "1441292_at"   "1441610_at"   "1441672_at"   "1441745_at"  
## [109] "1441793_at"   "1441981_at"   "1442209_at"   "1442715_at"  
## [113] "1443163_at"   "1443292_at"   "1443974_at"   "1443997_at"  
## [117] "1444522_at"   "1445368_at"   "1445408_at"   "1445438_at"  
## [121] "1445983_at"   "1446110_at"   "1446120_at"   "1446246_at"  
## [125] "1446249_at"   "1446748_at"   "1446869_at"   "1447616_at"  
## [129] "1447669_s_at" "1448241_at"   "1448613_at"   "1448745_s_at"
## [133] "1448952_at"   "1449237_at"   "1449367_at"   "1449959_x_at"
## [137] "1450251_a_at" "1450470_at"   "1450505_a_at" "1450539_at"  
## [141] "1450748_at"   "1450936_a_at" "1451050_at"   "1451054_at"  
## [145] "1451258_at"   "1451287_s_at" "1451601_a_at" "1451613_at"  
## [149] "1452543_a_at" "1453092_at"   "1453218_at"   "1453257_at"  
## [153] "1453435_a_at" "1453488_at"   "1453568_at"   "1453620_at"  
## [157] "1453820_at"   "1454058_at"   "1454632_at"   "1454762_at"  
## [161] "1455324_at"   "1455825_s_at" "1455889_at"   "1456183_at"  
## [165] "1456211_at"   "1456481_at"   "1456512_at"   "1456784_at"  
## [169] "1457254_x_at" "1457433_x_at" "1457518_at"   "1457557_at"  
## [173] "1458367_at"   "1458519_at"   "1458533_at"   "1459037_at"  
## [177] "1459647_at"   "1459833_x_at" "1459898_at"   "1460624_at"  
## [181] "1460674_at"

Solution 2/2

Obtaining the annotation of the up- and down- regulated genes into tables

up_table = aafTableAnn(up_ids, "mouse4302.db")
head(up_table)
saveHTML(up_table, file="up_table.html")
browseURL("up_table.html")

down_table = aafTableAnn(down_ids, "mouse4302.db")
head(down_table)
saveHTML(down_table, file="down_table.html")
browseURL("down_table.html")

## An object of class "aafTable"
## Slot "probeids":
## [1] "1416200_at"
## 
## Slot "table":
## $Probe
## An object of class "aafList"
## [[1]]
## An object of class "aafProbe"
## [1] "1416200_at"
## 
## 
## $Symbol
## An object of class "aafList"
## [[1]]
## [1] "Il33"
## attr(,"class")
## [1] "aafSymbol"
## 
## 
## $Description
## An object of class "aafList"
## [[1]]
## [1] "interleukin 33"
## attr(,"class")
## [1] "aafDescription"
## 
## 
## $Chromosome
## An object of class "aafList"
## [[1]]
## [1] "19"
## attr(,"class")
## [1] "aafChromosome"
## 
## 
## $`Chromosome Location`
## An object of class "aafList"
## [[1]]
## An object of class "aafChromLoc"
## [1] 29945789 29925113
## 
## 
## $GenBank
## An object of class "aafList"
## [[1]]
## [1] "NM_133775"
## attr(,"class")
## [1] "aafGenBank"
## 
## 
## $Gene
## An object of class "aafList"
## [[1]]
## An object of class "aafLocusLink"
## [1] 77125
## 
## 
## $UniGene
## An object of class "aafList"
## [[1]]
## [1] "Mm.182359"
## attr(,"class")
## [1] "aafUniGene"
## 
## 
## $PubMed
## An object of class "aafList"
## [[1]]
## An object of class "aafPubMed"
##   [1]  8889548 10349636 11042159 11076861 11217851 12466851 12477932
##   [8] 12819012 15475267 15489334 16141072 16141073 16286016 16602821
##  [15] 17185418 17492053 17623648 17675517 17881510 18003919 18023358
##  [22] 18250453 18268038 18450470 18552204 18566365 18603409 18667700
##  [29] 18799693 18802081 19248109 19451398 19465481 19508382 19553541
##  [36] 19559631 19661270 19666510 19684081 19750479 19841166 19892870
##  [43] 19933859 19950183 20035719 20042577 20385815 20412815 20427273
##  [50] 20501612 20534524 20634488 20689814 20693421 20937871 20939024
##  [57] 21190867 21220696 21239713 21239718 21267068 21268000 21281751
##  [64] 21308681 21349253 21357533 21422152 21454686 21469105 21494550
##  [71] 21515798 21557213 21642589 21646790 21677750 21703183 21703403
##  [78] 21734074 21797940 21835205 21887788 21945667 21949025 21949094
##  [85] 21972019 22013230 22013485 22119406 22142849 22180658 22198948
##  [92] 22258632 22267218 22270365 22294690 22307629 22323740 22329990
##  [99] 22331917 22370606 22371395 22460070 22542450 22585447 22634619
## [106] 22660580 22661085 22665806 22686327 22689946 22702477 22782692
## [113] 22802353 22922818 23006545 23071771 23093619 23132931 23148283
## [120] 23162017 23169007 23248269 23300625 23323935 23324173 23347081
## [127] 23363980 23397250 23403558 23418608 23496815 23499895 23523996
## [134] 23547117 23582173 23585480 23630360 23662055 23683462 23733876
## [141] 23810766 23837438 23892028 23894196 23911389 23918359 23945235
## [148] 23954132 23960191 24028396 24043894 24045639 24058536 24076135
## [155] 24076431 24105680 24194600 24205109 24220317 24257755 24356538
## [162] 24446518 24459820 24551140 24556514 24586149 24613091 24619410
## [169] 24675360 24730559 24786898 24860117 24860190 24892809 24982172
## [176] 24985397 25015831 25022964 25043027 25143444 25153903 25172162
## [183] 25278425 25313073 25429071 25458701 25472995 25500143 25504587
## [190] 25505285 25533952 25543045 25573803 25599561 25617223 25617473
## [197] 25660244 25661185 25676669 25683166 25693767 25714839 25714983
## [204] 25739051 25746970 25786179 25795135 25807992 25808546 25814531
## [211] 25829541 25847973 25857925 25870243 25930197 25941360 25944738
## [218] 25997709 26011644 26018806 26044350 26047701 26079807 26092469
## [225] 26100084 26200013 26230091 26243875 26244295 26249267 26251474
## [232] 26272855 26277897 26310268 26322482 26342029 26352378 26363151
## [239] 26365875 26386119 26428949 26473724 26489077 26490658 26514775
## [246] 26518437 26566691 26598236 26602156 26603638 26771472 26802241
## [253] 26811463 26872602 26872699 26908008 26943125 26987428 26991049
## [260] 27053610 27091974 27126934 27155324 27184849 27453471 27608599
## [267] 27626380
## 
## 
## $`Gene Ontology`
## An object of class "aafList"
## [[1]]
## An object of class "aafGO"
## [[1]][[1]]
## An object of class "aafGOItem"
## @id   "GO:0002281"
## @name "macrophage activation involved in immune response"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[2]]
## An object of class "aafGOItem"
## @id   "GO:0002282"
## @name "microglial cell activation involved in immune response"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[3]]
## An object of class "aafGOItem"
## @id   "GO:0002686"
## @name "negative regulation of leukocyte migration"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[4]]
## An object of class "aafGOItem"
## @id   "GO:0002826"
## @name "negative regulation of T-helper 1 type immune response"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[5]]
## An object of class "aafGOItem"
## @id   "GO:0002830"
## @name "positive regulation of type 2 immune response"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[6]]
## An object of class "aafGOItem"
## @id   "GO:0002830"
## @name "positive regulation of type 2 immune response"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[7]]
## An object of class "aafGOItem"
## @id   "GO:0005125"
## @name "cytokine activity"
## @type "Molecular Function"
## @evid "IDA"
## 
## [[1]][[8]]
## An object of class "aafGOItem"
## @id   "GO:0005125"
## @name "cytokine activity"
## @type "Molecular Function"
## @evid "IPI"
## 
## [[1]][[9]]
## An object of class "aafGOItem"
## @id   "GO:0005125"
## @name "cytokine activity"
## @type "Molecular Function"
## @evid "ISO"
## 
## [[1]][[10]]
## An object of class "aafGOItem"
## @id   "GO:0005576"
## @name "extracellular region"
## @type "Cellular Component"
## @evid "IEA"
## 
## [[1]][[11]]
## An object of class "aafGOItem"
## @id   "GO:0005615"
## @name "extracellular space"
## @type "Cellular Component"
## @evid "IEA"
## 
## [[1]][[12]]
## An object of class "aafGOItem"
## @id   "GO:0005634"
## @name "nucleus"
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## @evid "ISO"
## 
## [[1]][[13]]
## An object of class "aafGOItem"
## @id   "GO:0005654"
## @name "nucleoplasm"
## @type "Cellular Component"
## @evid "ISO"
## 
## [[1]][[14]]
## An object of class "aafGOItem"
## @id   "GO:0005694"
## @name "chromosome"
## @type "Cellular Component"
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## 
## [[1]][[15]]
## An object of class "aafGOItem"
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## 
## [[1]][[16]]
## An object of class "aafGOItem"
## @id   "GO:0006351"
## @name "transcription, DNA-templated"
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## 
## [[1]][[17]]
## An object of class "aafGOItem"
## @id   "GO:0010628"
## @name "positive regulation of gene expression"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[18]]
## An object of class "aafGOItem"
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## @evid "IEA"
## 
## [[1]][[19]]
## An object of class "aafGOItem"
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## @name "positive regulation of proteasomal ubiquitin-dependent protein catabolic process"
## @type "Biological Process"
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## 
## [[1]][[20]]
## An object of class "aafGOItem"
## @id   "GO:0032436"
## @name "positive regulation of proteasomal ubiquitin-dependent protein catabolic process"
## @type "Biological Process"
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## 
## [[1]][[21]]
## An object of class "aafGOItem"
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## @name "negative regulation of interferon-gamma production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[22]]
## An object of class "aafGOItem"
## @id   "GO:0032736"
## @name "positive regulation of interleukin-13 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[23]]
## An object of class "aafGOItem"
## @id   "GO:0032753"
## @name "positive regulation of interleukin-4 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[24]]
## An object of class "aafGOItem"
## @id   "GO:0032754"
## @name "positive regulation of interleukin-5 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[25]]
## An object of class "aafGOItem"
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## @name "positive regulation of interleukin-6 production"
## @type "Biological Process"
## @evid "IGI"
## 
## [[1]][[26]]
## An object of class "aafGOItem"
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## 
## [[1]][[27]]
## An object of class "aafGOItem"
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## @name "positive regulation of macrophage activation"
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## [[1]][[28]]
## An object of class "aafGOItem"
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## @name "positive regulation of transcription from RNA polymerase II promoter"
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## [[1]][[29]]
## An object of class "aafGOItem"
## @id   "GO:0050729"
## @name "positive regulation of inflammatory response"
## @type "Biological Process"
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## 
## [[1]][[30]]
## An object of class "aafGOItem"
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## @type "Biological Process"
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## 
## [[1]][[31]]
## An object of class "aafGOItem"
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## [[1]][[32]]
## An object of class "aafGOItem"
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## [[1]][[33]]
## An object of class "aafGOItem"
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## @name "microglial cell proliferation"
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## [[1]][[34]]
## An object of class "aafGOItem"
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## @name "positive regulation of chemokine secretion"
## @type "Biological Process"
## @evid "IDA"
## 
## [[1]][[35]]
## An object of class "aafGOItem"
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## @name "positive regulation of chemokine secretion"
## @type "Biological Process"
## @evid "ISO"
## 
## [[1]][[36]]
## An object of class "aafGOItem"
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## @name "extrinsic apoptotic signaling pathway"
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## @evid "IGI"
## 
## 
## 
## $Pathway
## An object of class "aafList"
## [[1]]
## An object of class "aafPathway"
## [[1]][[1]]
## An object of class "aafGOItem"
## @id     "04623"
## @name   "Cytosolic DNA-sensing pathway"
## @enzyme ""

## An object of class "aafTable"
## Slot "probeids":
## [1] "1417808_at"
## 
## Slot "table":
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## [[1]]
## An object of class "aafProbe"
## [1] "1417808_at"
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## 
## $Symbol
## An object of class "aafList"
## [[1]]
## [1] "2310050C09Rik"
## attr(,"class")
## [1] "aafSymbol"
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## 
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## [1] "RIKEN cDNA 2310050C09 gene"
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## [1] "3"
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## [1] "aafChromosome"
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## 
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## [1] "NM_025621"
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## [1] "aafGenBank"
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## 
## $Gene
## An object of class "aafList"
## [[1]]
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## 
## 
## $UniGene
## An object of class "aafList"
## [[1]]
## [1] "Mm.144259"
## attr(,"class")
## [1] "aafUniGene"
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## 
## $PubMed
## An object of class "aafList"
## [[1]]
## An object of class "aafPubMed"
## [1] 10349636 11042159 11076861 11217851 12466851 16141072 16141073 21267068
## 
## 
## $`Gene Ontology`
## An object of class "aafList"
## [[1]]
## An object of class "aafGO"
## [[1]][[1]]
## An object of class "aafGOItem"
## @id   "GO:0001533"
## @name "cornified envelope"
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## @evid "IBA"
## 
## [[1]][[2]]
## An object of class "aafGOItem"
## @id   "GO:0005198"
## @name "structural molecule activity"
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## 
## [[1]][[3]]
## An object of class "aafGOItem"
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## @evid "IBA"
## 
## [[1]][[4]]
## An object of class "aafGOItem"
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## @evid "IBA"
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## [[1]][[5]]
## An object of class "aafGOItem"
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## @evid "IBA"
## 
## [[1]][[6]]
## An object of class "aafGOItem"
## @id   "GO:0070062"
## @name "extracellular exosome"
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## @evid "ISO"
## 
## 
## 
## $Pathway
## An object of class "aafList"
## [[1]]
## An object of class "aafPathway"
## list()

Homework

Identify the top 10 biologically significant genes (i.e., by fold-change)
Identify the top 10 statistically significant genes (i.e., by p-value)
Identify the overlapping (if any) between the two sets of genes

Gene Expression Analysis Using R

Example: Interferon regulatory factor 6 (IRF6)

Ahmed Moustafa (amoustafa@aucegypt.edu)

January 27, 2018

Today’s Objectives

Cleft Lip and Palate 1/3

Cleft Lip and Palate 2/3

Cleft Lip and Palate 3/3

Question

Hint

Hypothesis

Why Microarray?

Original Paper

Experimental Design

Dataset

Loading

Checking

Exploring

Transforming

Multiple Plots 1/2

Multiple Plots 2/2

Boxplot

Clustering 1/2

Clustering 2/2

Comparing

Scatter 1/2

Scatter 2/2

Differentially Expressed Genes (DEGs)

Biological Significance (fold-change) 1/2

Biological Significance (fold-change) 2/2

Statistical Significance (p-value) 1/3

t-test

Case A

Case B

Statistical Significance (p-value) 2/3

Statistical Significance (p-value) 3/3

Volcano : Statistical & Biological 1/3

Volcano : Statistical & Biological 2/3

Volcano : Statistical & Biological 3/3

Filtering for DEGs 1/3

Filtering for DEGs 2/3

Filtering for DEGs 3/3

Exercise

Solution 1/2

Solution 2/2

Heatmap 1/5

Heatmap 2/5

Heatmap 3/5

Heatmap 4/5

Heatmap 5/5

Enhanced Heatmap 1/4

Enhanced Heatmap 2/4

Enhanced Heatmap 3/4

Enhanced Heatmap 4/4

Annnotation 1/3

Annnotation 2/3

Annnotation 3/3

Exercise

Solution 1/2

Solution 2/2

Homework

Thank you!